Apparatus, method and article of manufacture for visualizing patterns of change and behavior on a compute infrastructure

ABSTRACT

Provided herein are exemplary techniques for visualizing patterns of changes and behavior on a compute infrastructure wherein a Differential View user interface provides for complete visualization of infrastructure change and behavior and further provides interactive filters that identify and display patterns of change and behavior, on a graduated scale, for the compute infrastructure as a whole and for specific groups within the infrastructure.

CROSS REFERENCE TO RELATED APPLICATION(S)/CLAIM OF PRIORITY

This application claims the benefit of, and incorporates by reference inthe entirety, International Application Number PCT/US03/34370, filedOct. 29, 2003, which claims the benefit of U.S. Application No.60/422,005, filed Oct. 29, 2002, also incorporated in its entiretyherein.

This application relates to and incorporates by reference in theentirety, International Application Number PCT/US 02/18473, entitled“Apparatus, Method, and Article of Manufacture for Managing Change on aCompute Infrastructure,” filed Jun. 11, 2002.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

REFERENCE OF AN APPENDIX

Not applicable.

FIELD OF THE INVENTION

The present invention relates generally to compute and/or networkmanagement and more particularly to an improved system, method,apparatus, and article of manufacture for visualizing patterns ofchanges and behavior on a compute infrastructure such as the one shownin FIG. 10.

BACKGROUND OF THE INVENTION

Heretofore, compute infrastructure change visualization techniquesinvolve programmed alerting generated by user defined events onindividual technology components or processes. Determining whatcomponents have changed and isolating patterns of failure has been theresponsibility of the individuals tasked with responding to alarms. Asexpected, the process is often time-consuming and cumbersome.

Furthermore, the existing focus of alerts on component or processfailures undermines the ability of individuals to identify componentswith a pattern of success.

Accordingly, what is needed is a comprehensive way to visualize changeon a compute infrastructure, and more particularly, a solution thatdetects and presents patterns of both positive and negative change on acompute infrastructure.

SUMMARY OF THE INVENTION

The present invention (also called Differential View) addresses theaforementioned problems of the prior art by providing for, among otherthings, an improved apparatus, method and article of manufacture forvisualizing patterns of change and behavior on a compute infrastructure.Differential View provides for complete visualization of infrastructurechange and behavior and further provides interactive filters thatidentify and display patterns of change and behavior, on a graduatedscale, for the compute infrastructure as a whole and for specific groupswithin the infrastructure. This allows any type of compute data to beconsolidated and visualized; this view can occur pre- or post-databaseload, or without ever loading data to a database. Furthermore, theattribute-values may represent any defined test (unit, system,performance, or industrial process).

Other aspects, features and advantages of the present invention willbecome better understood with regard to the following description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring briefly to the drawings, exemplary embodiments of the presentinvention will be described with reference to the accompanying drawingsin which

FIG. 1 illustrates a graphical interface of an exemplary embodiment ofthe present invention.

FIG. 2 illustrates the group selection feature of an exemplaryembodiment of the present invention.

FIG. 3 illustrates the group analysis feature of an exemplary embodimentof the present invention.

FIG. 4 illustrates the baseline comparison feature of an exemplaryembodiment the present invention.

FIG. 5 illustrates the drill down feature of an exemplary embodiment ofthe present invention.

FIG. 6 illustrates an exemplary three-dimensional view of one embodimentof the present invention.

FIG. 7 illustrates the color intensity feature of an exemplaryembodiment of the present invention.

FIG. 8 illustrates an exemplary embodiment of the customizable timeframefeature of the present invention.

FIG. 9 illustrates the user color selection feature of an exemplaryembodiment of the present invention.

FIG. 10 illustrates an exemplary embodiment of a compute infrastructuresuitable in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Referring more specifically to the drawings, for illustrative purposesaspects of the present invention is depicted in the exemplaryembodiments generally shown in FIGS. 1-10. It will be appreciated thatthe illustrated embodiments may vary as to their details, for example,representative icons (a square may be a circle), configuration (theexact screen layout may be adjusted), etc., without departing from thebasic concepts disclosed herein. The following description, therefore,should not to be taken in a limiting sense.

High Level Description

FIG. 1 illustrates a graphical representation of an exemplary embodimentof the present invention. As shown, the graphical view includes severalunderlying support mechanisms including: Colorized Grid of Nodes (FIG.1-1.0) being monitored, grouped for ease of association (in thisexample, the white lines in the grid divide the nodes by location)colored by evaluation of change status. Note: The concept of Node is notlimited to a physical object and can be extended to a logical conceptlike a business process, object or application a map of nodes; Baselines(FIG. 1-2.0): a selection of sets of predefined node attribute valueswith which to evaluate node conformity; Groups (FIG. 1-3.0): userdefined node groupings for change and behavior pattern isolation; PieCharts (FIG. 1-4.0, 4.1): for providing quantitative percentage ofchange within the selected set of nodes for referential comparison; TimeFrame (FIG. 1-5.0, 5.1, 5.2): utilities from which to alter the timeframe evaluated and presented; Auto Focus (FIG. 1-6.0): a utility whichevaluates the groups to present those with the greatest deviation fromexpected values; Custom Color (FIG. 1-7.0): a utility to select thecolors in which the graduated values for change appear; Rotate (FIG.1-8.0): providing view control; Create Report t (FIG. 1-9.0): a reportgenerator.

Visualization

FIG. 2 illustrates the group selection progression of functionalitylisted in the description of FIG. 1. It presents the group patternidentification process which consists of the primary graphical view andsupporting mechanisms: Selection of Groups (FIG. 2-1.0), select thegroup to be distinguished from the enterprise node view; Identificationof Nodes within Group Selection (FIG. 2-2.0), nodes which belong to theselected Node Group are highlighted to be distinguished from the fullpopulation of nodes; Group Selection Pie Chart (FIG. 2-3.6) providesvisualization of the quantitative percentage of change within theselected set of nodes; Node View Pie Chart (FIG. 2-4.0) providesvisualization of the quantitative percentage of change in fullpopulation to provide a basis with which to compare the group to thewhole. This ability provides a means by which to isolate the groups withthe highest rate of change. The Auto Focus button (FIG. 2-5.0) whenclicked, will automatically select and present the group with the mostsignificant rate of change.

FIG. 3 progresses beyond group selection and into analysis of the groupselection through Baseline Comparison.¹ It is not necessary to select aGroup in order to select a baseline. One could look at a Baseline forpatterns of change or behavior across the enterprise node view; however,patterns are more easily tracked when using both the Baseline and aGroup. FIGS. 3 and 4 combined illustrate the use of Baseline compare toquickly analyze and isolate the set of attributes which are out of rangewithin a Group. Selection of Groups (FIG. 3-1.0), select the group to bedistinguished from the enterprise node view; Selection of Baseline (FIG.3-2.0), select the Baseline through which to filter the node group (thisexample provides a visualization of nodes in WEB-GRP1 and how they alignwith the pre-established attribute-value pairs in the WEB-PATCHESBaseline). Node View (FIG. 3-3.0) presents the group nodes with thestatus relative to the Baseline; Node View Pie Chart (FIG. 3-4.0)continually provides visualization of the quantitative percentage ofchange in full population. Group Selection Pie Chart (FIG. 3-5.0)provides visualization of the quantitative percentage of change withinthe baseline for the selected set of nodes (in this example, 100% ofWEB-GRP1 exactly match the WEB-PATCHES Baseline. This would quicklyallow a system administrator to dismiss WEB-PATCHES as a problem areaand allow him or her to look for other areas in which to find root causeof change.² Multiple Groups may be selected.

FIG. 4 illustrates the means with which to progress through theBaselines to identify the properties, or patterns, of the most intensechange in the infrastructure. The group selected remains as it was inFIG. 3, i.e., Web-GRP1. Since, as described in FIG. 3, the User learnedthat the Baseline WEB-PATCHES had no changes, they move to anotherBaseline in an effort to identify a pattern of the change. Selection ofBaseline (FIG. 4-1.0), select the Baseline through which to filter thenode group (this example provides a visualization of nodes in WEB-GRP1as filtered through the attribute-value associations of NT-PERF). NodeView (FIG. 4-2.0) presents the group nodes with the status relative tothe Baseline; Node View Pie Chart (FIG. 4-3.0) continually providesvisualization of the quantitative percentage of change in fullpopulation Group Selection Pie Chart (FIG. 4-4.0) provides visualizationof the quantitative percentage of change within the baseline for theselected set of nodes. Comparing the Node View Pie Chart to the GroupView Pie Chart indicates quickly that the percentage of change isgreater in the NT PERF Baseline than the greater population andindicates an area for further investigation.³ Multiple Baselines may beselected.

FIG. 5 depicts the drill down from FIG. 4, focusing specifically on theNode Group and Baseline selected at the point the User Drills Down. NodeGroup View (FIG. 5-1.0), presents the selected group nodes, delineatedby location, with the status relative to the Baseline. The drill-downview reduces the number of nodes in the map, while leaving the remainderof the screen and its corresponding functionality intact.

FIG. 6 illustrates alternate 3D views of Drill Down. 3D-Z Axis (FIG.6-1.0) is the power axis and can be configured by the User to representany key aspect of the nodes being monitored (e.g. CPU Power (3 ofCPUs*CPU Speed), # of Users, Revenue,)

Color

The color assigned to a node is determined using a weighted movingaverage. Increasing the time of the sampled data for each attributecreates an average. The greater the percentage of change against thataverage, the greater the deviation and the greater the color shift (e.g.Green to Red).

The delta time is used to compute a moving average for each sample. Timeis actually the number of samples back in time, e.g., if the Dailysample is selected (as shown in FIG. 6), a delta time of 5 equates tothe average of the last five days. The maximum and minimum of theaverages are used to compute the entire range of possibility.

For example, if a CPU attribute is selected and it is currently 25%, andthe last five days it was: 90%, 10%, 50% 50% and 50%, the min is 10%,the max is 90% and the moving average is (90+10+30+35+50)/5=43%. Since25 is less then 43% it will be on the green scale where 10 is brightgreen and 43 is the midway point to red. To compute the exact color ofgreen on the scale, 43−10 is 33 and 25−10=15, so 15/33 is the percentageof green on the scale. FIG. 7 depicts a graphical illustration of thispoint.

FIG. 8 identifies the radio button selections for time comparison (FIG.7-1.0) Daily, Weekly and Monthly. The timeframe can be customized byusing the Custom Timeframe Button (FIG. 7-2.0), this customization willallow complex time selections like each Monday between 2 PM and 5 PM.Sliding Sample Mean Time (FIG. 7-3.0) is used to allow the end user tochange the default moving average in the computation of changes forMetrics types of attributes.

User Color Selection

As shown in FIG. 9, a user can change the colors in their view accordingto the user preferences.

Compute Infrastructure

Finally, FIG. 10 illustrates an exemplary network/compute infrastructurehaving Managers (FIG. 10-1.0, 2.0, 2.1, 2.2), Managers with Gateways(FIG. 10-3.0), Gateways (FIG. 1-4.0), Managed Nodes with Agents (FIG.10-5.1, 5.2, 5.3 etc), Managed Nodes that are Agentless (FIG. 10-6.0,6.1, 6.2 etc), Software including application software, that can bemanaged like a node (FIG. 10-7.0, 7.1 etc.), and Special Devices thatcan be managed (FIG. 10-8.0, 8.1, etc).

CONCLUSION

Having now described embodiments of the present invention, it should beapparent to those skilled in the art that the foregoing is illustrativeonly and not limiting, having been presented by way of example only. Allthe features disclosed in this specification (including any accompanyingclaims, abstract, and drawings) may be replaced by alternative featuresserving the same purpose, and equivalents or similar purpose, unlessexpressly stated otherwise. Therefore, numerous other embodiments of themodifications thereof are contemplated as falling within the scope ofthe present invention as defined by the appended claims and equivalentsthereto.

The techniques may be implemented in hardware or software, or acombination of the two. Specifically, the techniques may be implementedin computer programs executing on programmable computers that eachinclude a processor, a storage medium readable by the processor(including volatile and non-volatile memory and/or storage elements), atleast one input device and one or more output devices. Program code isapplied to data entered using the input device to perform the functionsdescribed and to generate output information. The output information isapplied to one or more output devices. Each program is preferablyimplemented in a high level procedural or object oriented programminglanguage to communicate with a computer system, however, the programscan be implemented in assembly or machine language, if desired. In anycase, the language may be a compiled or interpreted language. Each suchcomputer program is preferably stored on a storage medium or device(e.g., CD-ROM, hard disk or magnetic diskette) that is readable by ageneral or special purpose programmable computer for configuring andoperating the computer when the storage medium or device is read by thecomputer to perform the procedures described in this document. Theinvention may also be considered to be implemented as acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner.

1. A method of visualizing patterns of change and behavior on a computeinfrastructure having a plurality of nodes, said method comprising:providing a set of color hues; providing predetermined rates of changeor behavior for each node of said compute infrastructure; associating acolor hue with a rate of node change or behavior; monitoring said nodesto determine said rate of node change or behavior of each node;displaying a colorized map of said nodes of said compute infrastructure;displaying a first quantitative percentage of change graphic associatedwith said nodes of said compute infrastructure; wherein for each of saidnodes, displaying said color hue associated with said monitored rate ofnode change or behavior;
 2. A method as in claim 1 further comprising:providing one or more logical groupings of said nodes, each groupinghaving common node attributes; selecting one of said logical nodegroupings; identifying on said colorized map said nodes of said selectedlogical grouping; displaying a second quantitative percentage of changegraphic having a percentage of change associated with said nodes of saidselected logical grouping.
 3. The method as in claim 1, furthercomprising displaying textual data on at least a portion of saidcolorized map, said textual data comprising attribute informationpertaining to said nodes of said compute infrastructure.
 4. The methodas in claim 2, further comprising: providing a set of baselineattributes to evaluate node conformity; selecting one of said baselineattributes; identifying on said colorized map said nodes conforming tosaid selected baseline attribute; displaying said second quantitativepercentage of change graphic having a percentage of change associatedwith said nodes conforming to said selected baseline attribute.
 5. Themethod as in claim 4, further comprising: displaying said colorized mapcomprising substantially of said nodes conforming to said selectedbaseline attribute.
 6. The method as in claim 5, further comprising:displaying a three-dimensional graphic comprising said nodes conformingto said selected baseline attribute.
 7. The method as in claim 1 whereinsaid first quantitative percentage of change graphic is a pie chart. 8.The method as in claim 2 wherein said second quantitative percentage ofchange graphic is a pie chart.
 9. The method as in claim 1 wherein saidfirst quantitative percentage of change graphic is a bar chart.
 10. Themethod as in claim 2 wherein said second quantitative percentage ofchange graphic is a bar chart.
 11. The method as in claim 1 wherein saidcolor hues are determined using a weighted moving average.
 12. Themethod as in claim 1 further comprising: defining a timeframe;monitoring said nodes to determine said rate of node change or behaviorof each node during said time frame.